Emerging Use of Social Media in Clinical Urology Practice in the 21st Century: Survey Study
Bibliographic record
Abstract
Background: Social media (So-Me) platforms are valuable resources for health care professionals and academics to discover, discuss, and distribute current advances in research and clinical practices, including technology trends. Objective: This study aims to assess the role of So-Me in urological practice in Saudi Arabia. It explores the influence of digital platforms on patient interaction, professional communication, decision-making, and education. Methods: The survey was conducted among 145 urologists from July 2021 to July 2022 following institutional review board approval. A questionnaire designed using the SurveyMonkey platform examined urologists' knowledge of So-Me. The survey was conducted using the CHERRIES (Checklist for Reporting Results of Internet E-Surveys) guidelines and was open for 17 weeks. Data analysis was performed using SPSS 21.0. Results: Of the 145 participants, 70% (n=102) were Saudi Arabians. The most common age groups were 30-40 (n=68, 46.8%) and 41-50 (n=61, 42.2%) years, with a gender distribution of 44.8% (n=65) women and 55.2% (n=80) men. A total of 61.5% (n=89) of urologists reported using So-Me accounts for professional purposes, with 54.9% (n=80) sharing health-related information. Social media enhanced patient connections beyond clinic visits for 55.8% (n=81) of respondents, while 57.2% (n=83) used it to provide educational resources. Additionally, 56.5% (n=82) believed So-Me facilitated patient feedback and improved their practice. In terms of professional communication, 60.6% (n=88) of urologists agreed that So-Me facilitated collaboration with colleagues, while 63.3% (n=92) used it to stay updated on the latest advances in urology. Furthermore, 62% (n=90) followed professional societies or journals on So-Me, and 63.3% (n=92) used it for continuing medical education. A majority (n=94, 64.7%) reported that So-Me influenced treatment decisions based on new research findings, and 85.3% (n=124) learned about novel technologies and treatment options through these platforms. Regression analysis showed a significant positive correlation between gender and social media usage patterns (R=0.653, R2=0.426), indicating that approximately 42.6% of the differences in usage patterns can be attributed to gender. However, the Pearson χ2 analysis showed that gender did not significantly affect most aspects of social media use, except information sharing and participating in online discussions (both P<.05). Conclusions: This study highlights the widespread use of So-Me among urologists in Saudi Arabia, underscoring its role in enhancing patient interaction, professional development, and clinical decision-making. Strategically designed health care programs using social media could improve and modernize professional and patient-centered care in Saudi Arabia through legislative assistance and guidelines.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".